A Multimodel Approach of Complex Systems Identification and Control Using Neural and Fuzzy Clustering Algorithms
Nesrine Elfelly, Jean‐Yves Dieulot, Pierre Borne, Mohamed Benrejeb · 2010
This paper deals with a new approach for complex systems modeling and control based on neural and fuzzy clustering algorithms. It aims to derive a base of local models describing the system in the whole operating domain. The implementation of this approach requires three main steps: 1) determination of the structure of the model-base, the number of models are found out by using Rival Penalized Competitive Learning (RPCL), and the operating clusters are selected referring to the fuzzy K-means algorithm, 2) parametric model identification using the clustering results 3) determination of the global system control parameters obtained by a fusion of local control parameters. The case of a second order nonlinear system is studied to illustrate the efficiency of the proposed approach.